1. Technical Field
This invention relates to industrial process controls, more particularly, for a method and apparatus for controlling multivariable, dynamic, nonlinear industrial processes.
2. Background Art
Industrial automation has continually strived towards attaining the optimum manner for controlling industrial processes in order to meet quality and production requirements. However, most modern industrial processes are complex requiring multiple control variables with interacting dynamics having time delays and lags, and nonlinearities. To handle such complex industrial processes, there have evolved various process control techniques.
Most current process control techniques determine the optimum operation of a process by monitoring one or more of the process's characteristics over time in order to adjust the operational parameters of the process. To compute the optimum operational parameters, especially in light of variations in the setpoint, system dynamics, and disturbances, these techniques may rely on a model of the plant process to predict the future behavior of the system. In some advanced techniques, this model, or part of it, is incorporated within a controller structure. The accuracy of these techniques relies on a precise dynamic model of the process. Such a model may not be available since some processes have uncertainties which cannot be modeled precisely or simply.
Recently, neural networks have become an attractive means for modeling complex processes. This is because a neural network has the inherent ability to approximate a multivariable nonlinear function. The neural network is also advantageous since it does not require a complete or precise understanding of the process. Rather, it can acquire a representation of the process through its capacity to be trained and to learn by example. A neural network has the additional capability of handling delayed variables and, hence, represent dynamic systems.
The application of neural networks in the process control area is a relatively recent development. Nevertheless, various neural-network control systems have been developed. One such type is a control system which uses neural networks in the well established model-predictive-control framework. Typically, these types of control systems use a controller, employing a model of the process, to determine the manipulated variable which will put the process at the target value. Process feedback is provided through a process-model-mismatch signal which is applied to the setpoint thereby compensating for unmodeled disturbances. This mismatch signal is the difference between the process output and a modeled process output generated by a neural network of the process.
The controller consists of a neural-network model and an optimizer. The neural-network model is used to predict the effect of possible manipulated variable trajectory on the process outputs over a future time trajectory taking into account present and recent past process input and output values. The optimizer uses this information to select values of the manipulated variables such that the process outputs optimally track the setpoints and satisfy a given set of constraints.
There are several limitations of this type of process control system. The primary limitation is that it does not handle effectively unmeasured load disturbances for a lag dominant process. Although the use of a model-error feedback gives the system the capability to handle well a dead-time dominant process, the method does not stabilize a non-self regulating or an open-loop-unstable process unless some additional feedback is applied. There is no proportional or derivative feedback, only a quasi-integral feedback action provided through the process-model-mismatch signal. Furthermore, the controller output-trajectory optimization is rerun at every control interval in order to determine the next manipulated variable change. This optimization may require substantial computation time requiring the interval between controller output updates to be undesirably large. This interval also adds further dead-time to the process dead-time thereby increasing the best achievable control error in response to an unmeasured load change.
It is an object of this invention to provide a robust and efficient process control system which accounts for the above mentioned limitations. More particularly, an optimal multivariable nonlinear control system which is robust, accommodates non-self regulating processes as well as pure dead-time processes, requires no on-line optimization, compensates to prevent upsets by measured loads, and combats unmeasured disturbances with high feedback gain.
Other general and specific objects of this invention will be apparent and evident from the accompanying drawings and the following description.